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Abstract Molecular Communication via Diffusion (MCvD) is a promising paradigm which enables nano-machines to communicate with each other. However, the reliability of existing communication systems degrades rapidly as the distance between the transmitters and the receivers grows. To solve this issue, relaying schemes must be implemented in practice. In this paper, we study two relaying schemes: In the first case, the relay node decodes the incoming signal symbol and forwards it to the receiver using a different types of molecules. Then, the receiver detects the information bits by only considering the molecules from the relay node. In the second case, the receiver considers both types of molecules sent from the transmitter and the relay node. Closed-form expression for the end-to-end bit error probability of the these two schemes are derived. Also, we obtain optimal relay location for both schemes. Moreover, we study the optimal threshold, concentration, baud-rate and timing offset to mitigate the overall Inter-Symbol Interference(ISI) and obtain maximum throughput in the relay network. [ABSTRACT FROM AUTHOR]

Abstract In diffusion-based molecular communication simple modulation techniques based on the concentration, type, or released time of molecules are commonly used. However, random delay of the diffused molecules in environment can cause some strong inter-symbol interference (ISI) among consecutive symbols. Due to the limited computational capabilities of nano-machines, sophisticated modulation and coding schemes cannot be used to resolve this issue. In this paper, we propose several new, yet simple, ISI-resistant coding schemes for diffusion-based communications over a one-dimensional noiseless channel. These schemes map the information bits into variable length codewords and place time-gaps between the consecutive codewords to mitigate ISI issue. Our analytical and simulation results indicate that the proposed schemes provide superior performances in terms of probability of error and throughput compared to the existing schemes. [ABSTRACT FROM AUTHOR]

Probabilistic models are one of the common approaches for classification. One way to create these models is to form a decomposable model by selecting a set of marginal distributions. Although decomposable models are attractive by having some desirable properties, they would face the over-fitting issue in model-based classification approaches. Considering this issue, we propose a new method for selecting a set of marginal distributions and creating a proper decomposable model while controlling the complexity. The obtained model will be able to capture the interdependencies among different features and can be used for classification. The proposed method is compared with three existing methods namely TAN and Averaged TAN classifiers and t-Cherry algorithm by focusing on internet traffic data. Experimental results show that our obtained model can effectively extract dependencies among features, and hence, its performance as a classifier is superior compared to other three methods. [ABSTRACT FROM AUTHOR]